𝐄𝐥𝐞𝐯𝐚𝐭𝐢𝐧𝐠 𝐀𝐞𝐫𝐢𝐚𝐥 𝐕𝐢𝐬𝐢𝐨𝐧: 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐚𝐞𝐫𝐢𝐚𝐥 𝐬𝐮𝐫𝐯𝐞𝐢𝐥𝐥𝐚𝐧𝐜𝐞 𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐯𝐢𝐬𝐢𝐨𝐧!

Thrilled to unveil my new project, where I fine-tuned a YOLO model to identify vehicles from drone footage!

Project Steps:

• Insttall Ultralytics
  • pip install ultralytics
• Install Opencv
  • pip install opencv-python
• Run fine-tune.py and download the best.py file, needed for detecting in mail.py
• run main.py

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:

𝐀𝐞𝐫𝐢𝐚𝐥 𝐕𝐞𝐡𝐢𝐜𝐥𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Leveraged Ultralytics YOLO models for robust detection of vehicles in drone footage.

𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐕𝐢𝐬𝐃𝐫𝐨𝐧𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: Applied fine-tuning techniques to enhance model accuracy using the rich VisDrone dataset.

𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Explored the potential of aerial vision for applications like traffic monitoring, disaster response, security, and urban planning.

𝐑𝐞𝐬𝐮𝐥𝐭𝐬:

• Increased accuracy through fine-tuning for specialized aerial surveillance.

• Demonstrated the model's efficiency in handling large-scale aerial footage while maintaining real-time processing speeds.

𝐈𝐦𝐩𝐚𝐜𝐭:

This project opens doors for more effective aerial monitoring and surveillance, offering valuable insights for security, urban development, and emergency response.